Explore the evidence.
Understand the reach.


 
 
 Today, companies are planning their own activities depending on efficiency and effectiveness. In order to have plans for the future activities they need historical data coming from outside and inside of the companies. However, this data is in huge amounts to understand easily. Since, this huge amount of data creates complexity in business for many industries like hospitality industry, reliable, accurate and fast access to this data is to be one of the greatest problems. Besides, management of this data is another big problem. In order to analyze this huge amount of data, Data Mining (DM) tools, can be used effectively. In this study, after giving brief definition about fundamentals of data mining, Chi Squared Automatic Interaction Detection (CHAID) algorithm, one of the mostly used DM tool, will be introduced. By CHAID algorithm, the most used materials in room cleaning process and the relations of these materials based on in a five star hotel data are tried to be determined. At the end of the analysis, it is seen that while some variables have strong relation with the number of rooms cleaned in the hotel, the others have no or weak relation.
 
 

Sustainable Development Goals (SDGs) are a set of goals for sustainable development which are consist of 17 goals that are expected to be achieved by 2030. 91% of Indonesia's territory is a rural area, consisting of more than 17,000 islands, almost 7500 villages. The villages contribute about 74% to the achievement of the national SDGs. Indonesia implements The Village SDGs which have 18 goals. The 18th goal is to achieve dynamic village institutions and adaptive village culture designed to accommodate various local cultures that characterize village management and are still applied by the community. Almost all district in Indonesia do not yet have a mapping of the status of achieving the Village SDGs. It will be easier for the district government to prioritize village development programs according to the needs of each village. In this study, research variables that measure 17 village goals will be used and grouped into 4 aspects, namely economic aspects, environmental aspects, health and education aspects and welfare aspects. Because village SDGs data is a mixture of numeric and categorical, ROCK ensemble analysis is used which is a grouping method for mixed data. In this study, it was found that the majority of villages in Sidoarjo Regency were still lagging behind in the fields of economy, health, education, and community welfare. This can be seen in the number of villages that are included in the low group based on the results of the analysis.

Marriage is one of the most important social institutions, as it greatly influences the stability of families, population, and socio-economic well-being. The changing trends of marriages and divorces in South Africa confirm the importance of predictive analysis on the stability of marriages. Marriage duration prediction is a challenging task, as it is affected by a number of factors. Recent developments in machine learning techniques provide new opportunities to model this problem. This experiment used the following seven different classifications: Random Forest, Gradient Boosting, XGBoost, Logistic Regression, Support Vector Machine, K Nearest Neighbours, and Decision Tree, which aimed to forecast the categories of marriage length (short, medium, and long). The models used the following metrics: accuracy, precision, recall, F1-score, macro ROC-AUC, and the ROC curve. The results showed that ensemble-based approaches performed better than conventional classifiers. Gradient Boosting had the best performance in macro ROC AUC and was best at discriminating classes, especially for long and medium-length marriages. Random Forest had the best overall accuracy and was best at balancing classes. XGBoost had a similar performance to that of Random Forest in predicting. SVM had a high precision value but a lower recall value. Decision Trees and KNN had poor performance. Gradient Boosting was found to be the best-performing method for multi-class prediction of duration of marriage and achieved a balance between sensitivity and specificity. The results obtained from these studies prove that ensemble learning methods can be effectively utilized for modelling complex societal events.

Marriage is one of the most important social institutions, as it greatly influences the stability of families, population, and socio-economic well-being. The changing trends of marriages and divorces in South Africa confirm the importance of predictive analysis on the stability of marriages. Marriage duration prediction is a challenging task, as it is affected by a number of factors. Recent developments in machine learning techniques provide new opportunities to model this problem. This experiment used the following seven different classifications: Random Forest, Gradient Boosting, XGBoost, Logistic Regression, Support Vector Machine, K Nearest Neighbours, and Decision Tree, which aimed to forecast the categories of marriage length (short, medium, and long). The models used the following metrics: accuracy, precision, recall, F1-score, macro ROC-AUC, and the ROC curve. The results showed that ensemble-based approaches performed better than conventional classifiers. Gradient Boosting had the best performance in macro ROC AUC and was best at discriminating classes, especially for long and medium-length marriages. Random Forest had the best overall accuracy and was best at balancing classes. XGBoost had a similar performance to that of Random Forest in predicting. SVM had a high precision value but a lower recall value. Decision Trees and KNN had poor performance. Gradient Boosting was found to be the best-performing method for multi-class prediction of duration of marriage and achieved a balance between sensitivity and specificity. The results obtained from these studies prove that ensemble learning methods can be effectively utilized for modelling complex societal events.

Sustainable Development Goals (SDGs) are a set of goals for sustainable development which are consist of 17 goals that are expected to be achieved by 2030. 91% of Indonesia's territory is a rural area, consisting of more than 17,000 islands, almost 7500 villages. The villages contribute about 74% to the achievement of the national SDGs. Indonesia implements The Village SDGs which have 18 goals. The 18th goal is to achieve dynamic village institutions and adaptive village culture designed to accommodate various local cultures that characterize village management and are still applied by the community. Almost all district in Indonesia do not yet have a mapping of the status of achieving the Village SDGs. It will be easier for the district government to prioritize village development programs according to the needs of each village. In this study, research variables that measure 17 village goals will be used and grouped into 4 aspects, namely economic aspects, environmental aspects, health and education aspects and welfare aspects. Because village SDGs data is a mixture of numeric and categorical, ROCK ensemble analysis is used which is a grouping method for mixed data. In this study, it was found that the majority of villages in Sidoarjo Regency were still lagging behind in the fields of economy, health, education, and community welfare. This can be seen in the number of villages that are included in the low group based on the results of the analysis.


 
 
 Today, companies are planning their own activities depending on efficiency and effectiveness. In order to have plans for the future activities they need historical data coming from outside and inside of the companies. However, this data is in huge amounts to understand easily. Since, this huge amount of data creates complexity in business for many industries like hospitality industry, reliable, accurate and fast access to this data is to be one of the greatest problems. Besides, management of this data is another big problem. In order to analyze this huge amount of data, Data Mining (DM) tools, can be used effectively. In this study, after giving brief definition about fundamentals of data mining, Chi Squared Automatic Interaction Detection (CHAID) algorithm, one of the mostly used DM tool, will be introduced. By CHAID algorithm, the most used materials in room cleaning process and the relations of these materials based on in a five star hotel data are tried to be determined. At the end of the analysis, it is seen that while some variables have strong relation with the number of rooms cleaned in the hotel, the others have no or weak relation.